Utilizing Webs to Share Ancestral and Intergenerational Teachings: The Process of Co-Building an Online Digital Repository in Partnership with Indigenous Communities
Bibliographic record
Abstract
Indigenous knowledge and wisdom continue to guide food and land practices, which may be key to lowering high rates of diabetes and obesity among Indigenous communities. The purpose of this paper is to describe how Indigenous, ancestral, and wise practices around food and land can best be reclaimed, revitalized, and reinvented through the use of an online digital platform. Key informant interviews and focus groups were conducted in order to identify digital data needs for food and land practices. Participants included Indigenous key informants, ranging from elders to farmers. Key questions included: (1) How could an online platform be deemed suitable for Indigenous communities to catalogue food wisdom? (2) What types of information would be useful to classify? (3) What other related needs exist? Researchers analyzed field notes, identified themes, and used a consensual qualitative research approach. Three themes were found, including a need for the appropriate use of Indigenous knowledges and sharing such online, a need for community control of Indigenous knowledges, and a need and desire to share wise practices with others online. An online Food Wisdom Repository that contributes to the health and wellbeing of Indigenous peoples through cultural continuity appears appropriate if it follows the outlined needs.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.025 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.014 | 0.012 |
| Scholarly communication | 0.014 | 0.021 |
| Open science | 0.002 | 0.026 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".